Towards Low-cost Sign Language Gesture Recognition Leveraging Wearables

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Tianming Zhao ◽  
Jian Liu ◽  
Yan Wang ◽  
Hongbo Liu ◽  
Yingying Chen
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Low Cost

Technological Aids for Deaf and Mute in Modern World

2020 ◽  
Vol 14 ◽  
Author(s):  
Vasu Mehra ◽  
Dhiraj Pandey ◽  
Aayush Rastogi ◽  
Aditya Singh ◽  
Harsh Preet Singh
Keyword(s):  
Sign Language ◽  
Gesture Recognition ◽  
Recognition System ◽  
Modern World ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Background Elimination ◽  
Sign Recognition ◽  
Technological Advances ◽  
Better Than

Background:: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective:: Developing a system for sign language recognition becomes essential for deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods:: The proposed system embedded with gesture recognition capability has been introduced here which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text as well as text to speech system is also introduced to further facilitate the grieved people. To get the best out of human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models which have been trained by using Tensor Flow and Keras library. Result:: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion:: It’s the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. Proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in identification of different gestures.

Alphabetical Gesture Recognition of American Sign Language using E-Voice Smart Glove

2020 ◽  
Author(s):  
Muhammad Saad Amin ◽  
Muhammad Talha Amin ◽  
Muhammad Yasir Latif ◽  
Ali Asghar Jathol ◽  
Nisar Ahmed ◽  
...  
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Gesture Recognition ◽  
American Sign

scholarly journals CrossGR

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Xinyi Li ◽  
Liqiong Chang ◽  
Fangfang Song ◽  
Ju Wang ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Low Cost ◽  
User Involvement ◽  
Recognition System ◽  
Training Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Target User ◽  
Order Of Magnitude ◽  
Training Examples

This paper focuses on a fundamental question in Wi-Fi-based gesture recognition: "Can we use the knowledge learned from some users to perform gesture recognition for others?". This problem is also known as cross-target recognition. It arises in many practical deployments of Wi-Fi-based gesture recognition where it is prohibitively expensive to collect training data from every single user. We present CrossGR, a low-cost cross-target gesture recognition system. As a departure from existing approaches, CrossGR does not require prior knowledge (such as who is currently performing a gesture) of the target user. Instead, CrossGR employs a deep neural network to extract user-agnostic but gesture-related Wi-Fi signal characteristics to perform gesture recognition. To provide sufficient training data to build an effective deep learning model, CrossGR employs a generative adversarial network to automatically generate many synthetic training data from a small set of real-world examples collected from a small number of users. Such a strategy allows CrossGR to minimize the user involvement and the associated cost in collecting training examples for building an accurate gesture recognition system. We evaluate CrossGR by applying it to perform gesture recognition across 10 users and 15 gestures. Experimental results show that CrossGR achieves an accuracy of over 82.6% (up to 99.75%). We demonstrate that CrossGR delivers comparable recognition accuracy, but uses an order of magnitude less training samples collected from the end-users when compared to state-of-the-art recognition systems.

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